Evidence map›Paper›PMID 37231770›Full record

ArticleEuropean psychiatry : the journal of the Association of European Psychiatrists2023

A multivariate approach to investigate the associations of electrophysiological indices with schizophrenia clinical and functional outcome.

Luigi Giuliani, Nikolaos Koutsouleris, Giulia Maria Giordano, Thomas Koenig, Armida Mucci, Andrea Perrottelli, Anne Reuf, Mario Altamura, Antonello Bellomo, Roberto Brugnoli and 8 more

Abstract read
In one paragraph

Article in European psychiatry : the journal of the Association of European Psychiatrists, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

18 authors.

Luigi GiulianiDepartment of Psychiatry, University of Campania Luigi Vanvitelli, Naples, Italy.
Nikolaos KoutsoulerisDepartment of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany.
Giulia Maria GiordanoDepartment of Psychiatry, University of Campania Luigi Vanvitelli, Naples, Italy.
Thomas KoenigTranslational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland.
Armida MucciDepartment of Psychiatry, University of Campania Luigi Vanvitelli, Naples, Italy.ORCID 0000-0002-6195-9456
Andrea PerrottelliDepartment of Psychiatry, University of Campania Luigi Vanvitelli, Naples, Italy.ORCID 0000-0001-7771-1845
Anne ReufDepartment of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany.ORCID 0009-0009-2839-739X
Mario AltamuraPsychiatry Unit, Department of Medical Sciences, University of Foggia, Foggia, Italy.
Antonello BellomoPsychiatry Unit, Department of Medical Sciences, University of Foggia, Foggia, Italy.ORCID 0000-0002-0642-0802
Roberto BrugnoliDepartment of Neurosciences, Mental Health, and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University, Rome, Italy.
Giulio CorrivettiDepartment of Mental Health of ASL (Local Health Company) of Salerno, Salerno, Italy.
Giorgio Di LorenzoDepartment of Systems Medicine, Psychiatry and Clinical Psychology Unit, Tor Vergata University of Rome, Rome, Italy.ORCID 0000-0002-0576-4064
Paolo GirardiDepartment of Neurosciences, Mental Health and Sensory Organs, Suicide Prevention Center, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy.
Palmiero MonteleoneDepartment of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Section of Neuroscience, University of Salerno, Salerno, Italy.
Cinzia NioluDepartment of Systems Medicine, Psychiatry and Clinical Psychology Unit, Tor Vergata University of Rome, Rome, Italy.
Silvana GalderisiDepartment of Psychiatry, University of Campania Luigi Vanvitelli, Naples, Italy.ORCID 0000-0002-1592-7656
Mario MajDepartment of Psychiatry, University of Campania Luigi Vanvitelli, Naples, Italy.
Italian Network for Research on Psychoses

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDifferent electrophysiological (EEG) indices have been investigated as possible biomarkers of schizophrenia. However, these indices have a very limited use in clinical practice, as their associations with clinical and functional outcomes remain unclear. This study aimed to investigate the associations of multiple EEG markers with clinical variables and functional outcomes in subjects with schizophrenia (SCZs).

methodsResting-state EEGs (frequency bands and microstates) and auditory event-related potentials (MMN-P3a and N100-P3b) were recorded in 113 SCZs and 57 healthy controls (HCs) at baseline. Illness- and functioning-related variables were assessed both at baseline and at 4-year follow-up in 61 SCZs. We generated a machine-learning classifier for each EEG parameter (frequency bands, microstates, N100-P300 task, and MMN-P3a task) to identify potential markers discriminating SCZs from HCs, and a global classifier. Associations of the classifiers' decision scores with illness- and functioning-related variables at baseline and follow-up were then investigated.

resultsThe global classifier discriminated SCZs from HCs with an accuracy of 75.4% and its decision scores significantly correlated with negative symptoms, depression, neurocognition, and real-life functioning at 4-year follow-up.

conclusionsThese results suggest that a combination of multiple EEG alterations is associated with poor functional outcomes and its clinical and cognitive determinants in SCZs. These findings need replication, possibly looking at different illness stages in order to implement EEG as a possible tool for the prediction of poor functional outcome.

Indexed as

SchizophreniaBiomarkersElectroencephalographyEvent-Related Potentials, P300HumansBiomarkersEEGfunctional outcomemachine learningschizophrenia

Identifiers

PMID37231770
PMCPMC10304998

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.